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Posted By: | ANNA PALAIOLOGK |
Date: | Tue, 8 Nov 2016, 3:05 p.m. |
After a tough competition, in October of 2015 a group of eight organisations from Sweden, Germany, United Kingdom and Switzerland managed to secure funding of 3,3 million Euros from the European Union’s Horizon 2020 research and innovation programme, in order to take Europe’s CFD community one step closer to performing simulations in exascale environments. The project’s duration is 36 months (October 2015 – September 2018) The name of the project is ExaFLOW which stands for “Enabling Exascale Fluid Dynamics Simulations”. Computational Fluid Dynamics (CFD) is a prime contender for reaching exascale performance: In fluid dynamics there is virtually no limit to the size of the systems to be stu-died via numerical simulations, which can be exploited for extreme parallel scaling. Moreover, fluid flows are an essential element of many industrial and academic problems: A crude estimate shows that 10% of the energy use in the world is spent overcoming turbulent friction. As such, collaboration between software creators and European industries within automotive, manufacturing, aerospace, energy and health care is crucial. The goals of ExaFLOW comprise four key innovation areas, including aspects of mesh adaptivity, resilience, strong scaling at exascale through novel CG-HDG discretisations and I/O efficiency improvements. In all these areas, significant progress has been made since the project start. For instance, we have developed fault tolerance mechanisms, such that the new algorithm “survives” >90% of the errors that would otherwise have resulted in an execution failure; this all with very little overhead both in fault-free execution and in recovery. We are currently putting that capability into Nektar++, which will also include a multi-layer checkpoint approach, supplementing classic to-disk checkpoints with inexpensive “diskless checksum checkpoints” that may be sampled more frequently. Similar developments are also added to Nek5000 using an innovative checkpoint/revolve algorithm. For the improvement of IO strategies we implemented a new tool called FieldIOBenchmarker which is integrated in our applications, and allows to use XML, HDF5 and SIONlib. Finally, we have considered a number of different pre-conditioners (including Hypre based on PETSc), which are ready to be used in the forthcoming h- and p-type refinements in Nek5000 and Nektar++. Amongst the project partners there are leading European research groups from the Royal Institute of Technology (KTH) in Sweden, Imperial College London, the University of Southampton, the University of Stuttgart and from Ecole Polytechnique Federale de Lausanne (EPFL) together with three major European HPC centres: PDC at KTH in Sweden, who coordinates the project, the High Performance Computing Center of Stuttgart (HLRS) and the Edinburgh Parallel Computing Centre of the University of Edinburgh (EPCC). The two industrial partners who work on two different automotive use cases are McLaren Racing and the Automotive Simulation Center of Stuttgart who represents Opel in this project. All ExaFLOW innovations are clearly targeted to enhance the efficiency and exploitability of a number of existing and heavily used open-source codes on today’s largest-scale (and in the future exascale) systems: The spectral codes Nek5000 and Nektar++, and the finite difference codes OpenSBLI and NS3D. If you want to find out more about are project you can visit us in Salt Lake City at our booths at the Supercomputing 2016 Conference. Just approach booth number 1743 of the High Performance Computing Center of Stuttgart (HLRS) or booth number 701 of the Edinburgh Parallel Computing Centre of the University of Edinburgh (EPCC).
More information can also be found at our website http://exaflow-project.eu where you can subscribe to our newsletter, at our twitter account @ExaFLOWproject or requested via email at exaflow-contact@pdc.kth.se.
PROJECT DETAILS Start date: 01/10/2015 End date: 30/09/2018 Duration: 36 months Reference: GA no 671571 Call: H2020-FETHPC-2014
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